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  "metadata": {
    "colab": {
      "name": "机器学习kaggle案例：风控评分卡模型（Give Me Some Credit）.ipynb",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/824024445/KaggleCases/blob/master/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0kaggle%E6%A1%88%E4%BE%8B%EF%BC%9A%E9%A3%8E%E6%8E%A7%E8%AF%84%E5%88%86%E5%8D%A1%E6%A8%A1%E5%9E%8B%EF%BC%88Give_Me_Some_Credit%EF%BC%89.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dd1rlcTx9ute",
        "colab_type": "text"
      },
      "source": [
        "## 一、简介\n",
        "kaggle上经典的风控模型：通过预测未来两年内某人将面临财务困境的可能性，提高信用评分的现有水平"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N821GKWZQTQ8",
        "colab_type": "text"
      },
      "source": [
        "### 1.1 比赛描述"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_49StmvTQVO8",
        "colab_type": "text"
      },
      "source": [
        "银行在市场经济中起着至关重要的作用。他们决定谁可以获得融资以及在什么条件下获得投资决策。要使市场和社会发挥作用，个人和公司需要获得信贷。 \n",
        "\n",
        "信用评分算法，用于猜测违约概率，是银行用来确定是否应该授予贷款的方法。\n",
        "\n",
        "该竞赛要求参与者通过预测某人在未来两年内遇到财务困境的可能性来改进信用评分的现有技术水平。 \n",
        "\n",
        "本次竞赛的目标是建立一个借款人可以用来帮助做出最佳财务决策的模型。 250,000名借款人提供历史数据，奖金池为5,000美元（第一名为3,000美元，第二名为1,500美元，第三名为500美元）。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1XB4YrjQn0H",
        "colab_type": "text"
      },
      "source": [
        "### 1.2 比赛评估"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGn3wLhVQxhi",
        "colab_type": "text"
      },
      "source": [
        "使用AUC(Area Under \n",
        "ROC Curve)作为性能性能评估标准。意思是ROC曲线下的面积\n",
        "\n",
        "ROC全称是受试者工作特征。横坐标是假正例率（False Positive \n",
        "Rate），纵坐标是真正例率（True Positive \n",
        "Rate）"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyYhVEAh4ve2",
        "colab_type": "text"
      },
      "source": [
        "![替代文字](https://raw.githubusercontent.com/824024445/KaggleCases/master/img/GiveMeSomeCredict/1-2.jpg)\n",
        "![替代文字](https://raw.githubusercontent.com/824024445/KaggleCases/master/img/GiveMeSomeCredict/1-1.jpg)\n",
        "![替代文字](https://raw.githubusercontent.com/824024445/KaggleCases/master/img/GiveMeSomeCredict/1-3.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8S7nN3dbQ4kE",
        "colab_type": "text"
      },
      "source": [
        "### 1.3 数据描述"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAXFMkUOqmeN",
        "colab_type": "text"
      },
      "source": [
        "数据字典(取自Data Dictionary.xls文件）：\n",
        "\n",
        "在这里讲一下，kaggle每个竞赛都会提供\n",
        "- 数据字典（可能是在介绍中也可能是单独提供一个数据字典文件，就像这个案例一样）\n",
        "- 一个训练集\n",
        "- 一个测试集（不含目标值）\n",
        "- 一个提交文件的示例（本案例就是sampleEntry.csv文件）\n",
        "\n",
        "\n",
        "\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Variable Name</th>\n",
        "      <th>Description</th>\n",
        "      <th>Type</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>SeriousDlqin2yrs（目标值）</td>\n",
        "      <td>是否有超过90天或更长时间逾期未还的不良行为</td>\n",
        "      <td>Y/N（0为好1为坏）</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
        "      <td>可用额度比值</td>\n",
        "      <td>percentage</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>age</td>\n",
        "      <td>年龄</td>\n",
        "      <td>integer</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
        "      <td>逾期30-59天笔数</td>\n",
        "      <td>integer</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>DebtRatio</td>\n",
        "      <td>还款率(每月偿还债务，赡养费，生活费用).</td>\n",
        "      <td>percentage</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>MonthlyIncome</td>\n",
        "      <td>月收入</td>\n",
        "      <td>real</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>NumberOfOpenCreditLinesAndLoans</td>\n",
        "      <td>信贷数量</td>\n",
        "      <td>integer</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>NumberOfTimes90DaysLate</td>\n",
        "      <td>逾期90天笔数</td>\n",
        "      <td>integer</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>NumberRealEstateLoansOrLines</td>\n",
        "      <td>固定资产贷款量</td>\n",
        "      <td>integer</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
        "      <td>逾期60-89天笔数</td>\n",
        "      <td>integer</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>NumberOfDependents</td>\n",
        "      <td>家属数量</td>\n",
        "      <td>integer</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLdZ05Ljzgyy",
        "colab_type": "text"
      },
      "source": [
        "### 1.4 行业知识补充\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3arhStoe5k0M",
        "colab_type": "text"
      },
      "source": [
        "信用评分模型的书：[陈建的《信用评分模型与应用》](https://pan.baidu.com/s/1980a4)\n",
        "\n",
        "风控模型：先说分类，本文评分卡模型属于**A卡申请者评级模型**\n",
        "\n",
        "\n",
        "信用风险计量体系包括主体评级模型和债项评级两部分。主体评级和债项评级均有一系列评级模型组成，其中主体评级模型可用“四张卡”来表示，分别是A卡、B卡、C卡和F卡；债项评级模型通常按照主体的融资用途，分为企业融资模型、现金流融资模型和项目融资模型等。\n",
        "\n",
        "\n",
        "**A卡，又称为申请者评级模型**，个人用户申请相应的金融产品，对用户进行筛选分类，区分好坏用户，据此决定是否通过申请。在获客过程中用到的信用风险模型。从模型的角度来看，它会对用户未来一定周期内的逾期风险作预测，即模型会在用户授权的情况下收集用户多维度的信息，以此来预测逾期概率。预测的逾期概率被用于风控策略或者转换成信用评分，比如国外经典的FICO评分，国内的蚂蚁信用评分、京东小白评分、携程金融的程信分等。A卡评分除了用于决定是否通过用户的信用申请，还用于风险定价，比如额度、利率等\n",
        "\n",
        "\n",
        "**B卡，又称为行为评级模型**，在用户使用金融产品的过程中，根据用户的历史使用的行为数据对用户进行评级，对可能出现的逾期、延期等行为进行预测。用户拿到信用额度后，模型根据用户的贷中行为数据，进行风险水平的预测。本质上讲，这个模型是一个事件驱动的模型，在互联网金融领域，一般会比A卡的预测时间窗口要短，对用户的行为更为敏感。B卡除了可以用于高风险用户的拦截，也可以作为额度、利率调整的重要参考因素。\n",
        "\n",
        "\n",
        "**C卡，又称为催收评级模型**，对业务中的存量客户是否需要催收进行预测。例如当用户出现逾期时，机构应该先催谁，或者哪些用户不用催，就自动会把钱还回来。催收模型一定程度节约催收成本，提高回催率。\n",
        "\n",
        "\n",
        "**F卡，又称为欺诈评级模型**，对业务中的新用户可能存在的欺诈行为进行预测。根据用户提交的材料进行身份核实，确保用户不存在欺诈行为；\n",
        "\n",
        "风控建模所使用的客户数据主要分为：(1)信贷数据,(2)资质数据,(3)消费数据,(4)行为数据。\n",
        "\n",
        "![替代文字](https://raw.githubusercontent.com/824024445/KaggleCases/master/img/GiveMeSomeCredict/credict-1.png)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKN-UDqe_Xgj",
        "colab_type": "text"
      },
      "source": [
        "风控中常用的模型  \n",
        "![替代文字](https://raw.githubusercontent.com/824024445/KaggleCases/master/img/GiveMeSomeCredict/1-4.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UeT2__n_g8N",
        "colab_type": "text"
      },
      "source": [
        "就先补充这么多吧。跟细致的以后再写。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxcjRhwxRCN6",
        "colab_type": "text"
      },
      "source": [
        "## 二、代码"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Q0gblmvUaC-",
        "colab_type": "text"
      },
      "source": [
        "### 2.1 获取数据"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SErcnYPBUds7",
        "colab_type": "text"
      },
      "source": [
        "#### 2.1.1 下载数据\n",
        "我写了一个小函数来实现数据的下载，数据全都是官网原版数据，我存到了我的github上。（https://github.com/824024445/KaggleCases）  \n",
        "\n",
        "所有数据都下载到了你当前文件夹下的datasets文件下，每个案例涉及到的数据全部下载到了以该案例命名的文件夹下。  \n",
        "\n",
        "我所有的kaggle案例的博客，下载数据均会使用这个函数，只需要修改前两个常量即可。\n",
        "> 注：此函数只用于下载数据，函数在该代码框内就运行了。不再用到其它代码中，包括常量，也不会用在其他地方。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bg555-XqUi1J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import zipfile\n",
        "from six.moves import urllib\n",
        "\n",
        "FILE_NAME = \"GiveMeSomeCredit.zip\" #文件名\n",
        "DATA_PATH =\"datasets/GiveMeSomeCredit\" #存储文件的文件夹，取跟文件相同（相近）的名字便于区分\n",
        "DATA_URL = \"https://github.com/824024445/KaggleCases/blob/master/datasets/\" + FILE_NAME + \"?raw=true\"\n",
        "\n",
        "\n",
        "def fetch_data(data_url=DATA_URL, data_path=DATA_PATH, file_name=FILE_NAME):\n",
        "    if not os.path.isdir(data_path): #查看当前文件夹下是否存在\"datasets/GiveMeSomeCredit\"，没有的话创建\n",
        "        os.makedirs(data_path)\n",
        "    zip_path = os.path.join(data_path, file_name) #下载到本地的文件的路径及名称\n",
        "    # urlretrieve()方法直接将远程数据下载到本地\n",
        "    urllib.request.urlretrieve(data_url, zip_path) #第二个参数zip_path是保存到的本地路径\n",
        "    data_zip = zipfile.ZipFile(zip_path)\n",
        "    data_zip.extractall(path=data_path) #什么参数都不输入就是默认解压到当前文件,为了保持统一，是泰坦尼克的数据就全部存到GiveMeSomeCredit文件夹下\n",
        "    data_zip.close()\n",
        "fetch_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REU3OiPRUghR",
        "colab_type": "text"
      },
      "source": [
        "#### 2.1.2 读取数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9e2Ujw6o-IH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "52156e0f-14c4-4422-efae-6cfa2a2edcd9"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "train_df = pd.read_csv(\"datasets/GiveMeSomeCredit/cs-training.csv\")\n",
        "test_df = pd.read_csv(\"datasets/GiveMeSomeCredit/cs-test.csv\")\n",
        "combine=[train_df, test_df]\n",
        "train_df.head()"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyZWNrToPerD",
        "colab_type": "text"
      },
      "source": [
        "### 2.2 观察数据"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_8lx_CO6PlP4",
        "colab_type": "text"
      },
      "source": [
        "#### 2.2.1 info()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpGf5JW4qy0M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "475de8a9-7776-4897-83c6-0755f5230656"
      },
      "source": [
        "train_df.info()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150000 entries, 0 to 149999\n",
            "Data columns (total 12 columns):\n",
            "Unnamed: 0                              150000 non-null int64\n",
            "SeriousDlqin2yrs                        150000 non-null int64\n",
            "RevolvingUtilizationOfUnsecuredLines    150000 non-null float64\n",
            "age                                     150000 non-null int64\n",
            "NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64\n",
            "DebtRatio                               150000 non-null float64\n",
            "MonthlyIncome                           120269 non-null float64\n",
            "NumberOfOpenCreditLinesAndLoans         150000 non-null int64\n",
            "NumberOfTimes90DaysLate                 150000 non-null int64\n",
            "NumberRealEstateLoansOrLines            150000 non-null int64\n",
            "NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64\n",
            "NumberOfDependents                      146076 non-null float64\n",
            "dtypes: float64(4), int64(8)\n",
            "memory usage: 13.7 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jS-5OIraP1HH",
        "colab_type": "text"
      },
      "source": [
        "观察到：\n",
        "- \"MonthlyIncome\"和\"NumberOfDependents\"有空值。等会数据清洗把空值处理了。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Xb8oX4liHFN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "c1b26a7d-38f4-4489-af35-9bedbb185829"
      },
      "source": [
        "test_df.info()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 101503 entries, 0 to 101502\n",
            "Data columns (total 12 columns):\n",
            "Unnamed: 0                              101503 non-null int64\n",
            "SeriousDlqin2yrs                        0 non-null float64\n",
            "RevolvingUtilizationOfUnsecuredLines    101503 non-null float64\n",
            "age                                     101503 non-null int64\n",
            "NumberOfTime30-59DaysPastDueNotWorse    101503 non-null int64\n",
            "DebtRatio                               101503 non-null float64\n",
            "MonthlyIncome                           81400 non-null float64\n",
            "NumberOfOpenCreditLinesAndLoans         101503 non-null int64\n",
            "NumberOfTimes90DaysLate                 101503 non-null int64\n",
            "NumberRealEstateLoansOrLines            101503 non-null int64\n",
            "NumberOfTime60-89DaysPastDueNotWorse    101503 non-null int64\n",
            "NumberOfDependents                      98877 non-null float64\n",
            "dtypes: float64(5), int64(7)\n",
            "memory usage: 9.3 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2_mIsFAPrHu",
        "colab_type": "text"
      },
      "source": [
        "#### 2.2.2 decribe()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oOhZU0lLa3H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "665b8a22-40fa-475c-c266-b42205a33bce"
      },
      "source": [
        "#decribe查看数值型数据的信息.没有非数值型的数据，所以不使用describe(include=['O'])查看非数值型数据了。\n",
        "train_df.describe()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>age</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>DebtRatio</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>1.202690e+05</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>146076.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75000.500000</td>\n",
              "      <td>0.066840</td>\n",
              "      <td>6.048438</td>\n",
              "      <td>52.295207</td>\n",
              "      <td>0.421033</td>\n",
              "      <td>353.005076</td>\n",
              "      <td>6.670221e+03</td>\n",
              "      <td>8.452760</td>\n",
              "      <td>0.265973</td>\n",
              "      <td>1.018240</td>\n",
              "      <td>0.240387</td>\n",
              "      <td>0.757222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43301.414527</td>\n",
              "      <td>0.249746</td>\n",
              "      <td>249.755371</td>\n",
              "      <td>14.771866</td>\n",
              "      <td>4.192781</td>\n",
              "      <td>2037.818523</td>\n",
              "      <td>1.438467e+04</td>\n",
              "      <td>5.145951</td>\n",
              "      <td>4.169304</td>\n",
              "      <td>1.129771</td>\n",
              "      <td>4.155179</td>\n",
              "      <td>1.115086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>37500.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029867</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.175074</td>\n",
              "      <td>3.400000e+03</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75000.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.154181</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.366508</td>\n",
              "      <td>5.400000e+03</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112500.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.559046</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.868254</td>\n",
              "      <td>8.249000e+03</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>50708.000000</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>329664.000000</td>\n",
              "      <td>3.008750e+06</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Unnamed: 0  ...  NumberOfDependents\n",
              "count  150000.000000  ...       146076.000000\n",
              "mean    75000.500000  ...            0.757222\n",
              "std     43301.414527  ...            1.115086\n",
              "min         1.000000  ...            0.000000\n",
              "25%     37500.750000  ...            0.000000\n",
              "50%     75000.500000  ...            0.000000\n",
              "75%    112500.250000  ...            1.000000\n",
              "max    150000.000000  ...           20.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INvbxUwkQT10",
        "colab_type": "text"
      },
      "source": [
        "观察到：\n",
        "- \"NumberOfDependents\" 有50%以上的人没有家属，离散值较大，选取众数填充null"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFznNXxNPuCZ",
        "colab_type": "text"
      },
      "source": [
        "#### 2.2.3 corr()查找关联"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tDiDy8TL231",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7c33dd5c-d29a-4c44-948a-23b02723f4b0"
      },
      "source": [
        "#查找关联(后面清洗数据的时候也要经常用的，用来比较效果) \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "corr_matrix = train_df.corr()\n",
        "print(corr_matrix[\"SeriousDlqin2yrs\"].sort_values(ascending=False))\n",
        "# 下面的代码是图形化地展示各特征之间的相关性\n",
        "fig, ax = plt.subplots(figsize=(12,12))\n",
        "sns.heatmap(corr_matrix,xticklabels=corr_matrix.columns,yticklabels=corr_matrix.columns,annot=True)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SeriousDlqin2yrs                        1.000000\n",
            "NumberOfTime30-59DaysPastDueNotWorse    0.125587\n",
            "NumberOfTimes90DaysLate                 0.117175\n",
            "NumberOfTime60-89DaysPastDueNotWorse    0.102261\n",
            "Unnamed: 0                              0.002801\n",
            "RevolvingUtilizationOfUnsecuredLines   -0.001802\n",
            "NumberRealEstateLoansOrLines           -0.007038\n",
            "DebtRatio                              -0.007602\n",
            "NumberOfDependents                     -0.013881\n",
            "MonthlyIncome                          -0.018002\n",
            "NumberOfOpenCreditLinesAndLoans        -0.029669\n",
            "age                                    -0.115386\n",
            "Name: SeriousDlqin2yrs, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6dc5f0ad30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kpPP26Z8rF3nz5sHf3598+fKSEJ94RY6lZcvGfDf2RwDWrN1IocKFCA4OPC/d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\nlolYRERERETEGMtELCIiIiIiYoz9f4OfuYf/HsUVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GqJVRd6VmPh",
        "colab_type": "text"
      },
      "source": [
        "查找SeriousDlqin2yrs（目标值，越小越好）与其它特征的相关性\n",
        "- 其余特征，各特征相关性超过60%的就可以只用一个特征建模，此案例中，NumberOfTime30-59DaysPastDueNotWorse，NumberOfTimes90DaysLate，NumberOfTime60-89DaysPastDueNotWorse可以只用一个"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQgPio2pPNFg",
        "colab_type": "text"
      },
      "source": [
        "### 2.3 数据清洗\n",
        "清晰地还不是很好，过段时间我会重新整理的。其实，如果是纯自己探索的话，观察数据和数据清洗应该是一直交替进行的"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlWiJr_1Y-NY",
        "colab_type": "text"
      },
      "source": [
        "#### 2.3.1 缺失值处理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2dyCaGOPPTv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "7298674a-5c3f-4f11-8c48-4b9e7e403a4c"
      },
      "source": [
        "for data in combine:\n",
        "  data[\"MonthlyIncome\"].fillna(data[\"MonthlyIncome\"].mean(), inplace=True)\n",
        "  data[\"NumberOfDependents\"].fillna(data[\"MonthlyIncome\"].mode()[0], inplace=True)\n",
        "#查看一下替换后的数据，嗯，没有空值了\n",
        "train_df.info()"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150000 entries, 0 to 149999\n",
            "Data columns (total 12 columns):\n",
            "Unnamed: 0                              150000 non-null int64\n",
            "SeriousDlqin2yrs                        150000 non-null int64\n",
            "RevolvingUtilizationOfUnsecuredLines    150000 non-null float64\n",
            "age                                     150000 non-null int64\n",
            "NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64\n",
            "DebtRatio                               150000 non-null float64\n",
            "MonthlyIncome                           150000 non-null float64\n",
            "NumberOfOpenCreditLinesAndLoans         150000 non-null int64\n",
            "NumberOfTimes90DaysLate                 150000 non-null int64\n",
            "NumberRealEstateLoansOrLines            150000 non-null int64\n",
            "NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64\n",
            "NumberOfDependents                      150000 non-null float64\n",
            "dtypes: float64(4), int64(8)\n",
            "memory usage: 13.7 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsN_paEsZEYm",
        "colab_type": "text"
      },
      "source": [
        "#### 2.3.2 异常值处理"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xB_UAlDH1vdO",
        "colab_type": "text"
      },
      "source": [
        "NumberOfDependents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UuvjauItkJZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "94e1670f-03fc-4476-dee5-46d4e9f15bbe"
      },
      "source": [
        "#可以看到，家属数量居然有6670的,而且数量还不少，占到了2.6%,先用平均值填补吧\n",
        "train_df.NumberOfDependents.value_counts()"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.000000       86902\n",
              "1.000000       26316\n",
              "2.000000       19522\n",
              "3.000000        9483\n",
              "6670.221237     3924\n",
              "4.000000        2862\n",
              "5.000000         746\n",
              "6.000000         158\n",
              "7.000000          51\n",
              "8.000000          24\n",
              "9.000000           5\n",
              "10.000000          5\n",
              "13.000000          1\n",
              "20.000000          1\n",
              "Name: NumberOfDependents, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgj9OT86y3NS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "76e00300-0273-48c9-e969-164f6e473ae6"
      },
      "source": [
        "##填补前先看一下家属数和目标值的相关性，以便看一下效果,没处理前相关度-0.013881（我把所有的相关性列出来，是为了好随时查看其它几个的相关性）\n",
        "corr_matrix = train_df.corr()\n",
        "corr_matrix[\"SeriousDlqin2yrs\"].sort_values(ascending=False)"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SeriousDlqin2yrs                        1.000000\n",
              "NumberOfTime30-59DaysPastDueNotWorse    0.125587\n",
              "NumberOfTimes90DaysLate                 0.117175\n",
              "NumberOfTime60-89DaysPastDueNotWorse    0.102261\n",
              "Unnamed: 0                              0.002801\n",
              "RevolvingUtilizationOfUnsecuredLines   -0.001802\n",
              "NumberRealEstateLoansOrLines           -0.007038\n",
              "DebtRatio                              -0.007602\n",
              "NumberOfDependents                     -0.013881\n",
              "MonthlyIncome                          -0.018002\n",
              "NumberOfOpenCreditLinesAndLoans        -0.029669\n",
              "age                                    -0.115386\n",
              "Name: SeriousDlqin2yrs, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKy07nr0vJnm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "a8f48a1b-33eb-4950-e6b7-7cbc93108cdc"
      },
      "source": [
        "for data in combine:\n",
        "  data[\"NumberOfDependents\"][data[\"NumberOfDependents\"]>30] = 0\n",
        "  \n",
        "corr_matrix = train_df.corr()\n",
        "corr_matrix[\"SeriousDlqin2yrs\"].sort_values(ascending=False)\n",
        "#修改异常值后\"NumberOfDependents\"的相关性达到了0.046869"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SeriousDlqin2yrs                        1.000000\n",
              "NumberOfTime30-59DaysPastDueNotWorse    0.125587\n",
              "NumberOfTimes90DaysLate                 0.117175\n",
              "NumberOfTime60-89DaysPastDueNotWorse    0.102261\n",
              "NumberOfDependents                      0.046869\n",
              "Unnamed: 0                              0.002801\n",
              "RevolvingUtilizationOfUnsecuredLines   -0.001802\n",
              "NumberRealEstateLoansOrLines           -0.007038\n",
              "DebtRatio                              -0.007602\n",
              "MonthlyIncome                          -0.018002\n",
              "NumberOfOpenCreditLinesAndLoans        -0.029669\n",
              "age                                    -0.115386\n",
              "Name: SeriousDlqin2yrs, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WgIGkCf1rgU",
        "colab_type": "text"
      },
      "source": [
        "age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PX7RL2DglY8R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "04c3bc5c-e1c7-4008-813f-2f70e3a3d2dd"
      },
      "source": [
        "train_df = train_df[train_df[\"age\"]>18]\n",
        "test_df = test_df[test_df[\"age\"]>18]\n",
        "\n",
        "combine = [train_df, test_df]\n",
        "train_df[train_df[\"age\"]<18]"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>age</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>DebtRatio</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Unnamed: 0, SeriousDlqin2yrs, RevolvingUtilizationOfUnsecuredLines, age, NumberOfTime30-59DaysPastDueNotWorse, DebtRatio, MonthlyIncome, NumberOfOpenCreditLinesAndLoans, NumberOfTimes90DaysLate, NumberRealEstateLoansOrLines, NumberOfTime60-89DaysPastDueNotWorse, NumberOfDependents]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCD3J68X3A-r",
        "colab_type": "text"
      },
      "source": [
        "#### 2.3.3 创建新特征"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G06vLwAY3DmQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for data in combine:\n",
        "  data[\"CombinedDefaulted\"] = data[\"NumberOfTimes90DaysLate\"] + data[\"NumberOfTime60-89DaysPastDueNotWorse\"] + data[\"NumberOfTime30-59DaysPastDueNotWorse\"]\n",
        "  data.loc[(data[\"CombinedDefaulted\"] >= 1), \"CombinedDefaulted\"] = 1\n",
        "  data[\"CombinedCreditLoans\"] = data[\"NumberOfOpenCreditLinesAndLoans\"] + data[\"NumberRealEstateLoansOrLines\"]\n",
        "\n",
        "  data[\"CombinedCreditLoans\"] = data[\"NumberOfOpenCreditLinesAndLoans\"] + data[\"NumberRealEstateLoansOrLines\"]\n",
        "  data.loc[(data[\"CombinedCreditLoans\"] <= 5), \"CombinedCreditLoans\"] = 0\n",
        "  data.loc[(data[\"CombinedCreditLoans\"] > 5), \"CombinedCreditLoans\"] = 1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PiGX_Bzs4S8O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "bd6ec406-036a-461d-88b1-8931c2e675cc"
      },
      "source": [
        "train_df.corr()[\"SeriousDlqin2yrs\"][[\"CombinedDefaulted\", \"CombinedCreditLoans\"]]"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CombinedDefaulted      0.314415\n",
              "CombinedCreditLoans   -0.054778\n",
              "Name: SeriousDlqin2yrs, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5jANJKJZG33",
        "colab_type": "text"
      },
      "source": [
        "### 2.4 模型和预测"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUyHB3efjVKf",
        "colab_type": "text"
      },
      "source": [
        "数据集要再切分一下，前面的test是用来最终测试的，没有目标值，提交到kaggle之后它会返回给你一个AUC成绩，相当于评价泛化能力。而现在首先要自己评价当前模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PYcKox7jUEq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "attributes=[\"SeriousDlqin2yrs\", 'age','NumberOfTime30-59DaysPastDueNotWorse','NumberOfDependents','MonthlyIncome',\"CombinedDefaulted\",\"CombinedCreditLoans\"]\n",
        "sol=['SeriousDlqin2yrs']\n",
        "\n",
        "attributes2 = [\"Unnamed: 0\", 'age','NumberOfTime30-59DaysPastDueNotWorse','NumberOfDependents','MonthlyIncome',\"CombinedDefaulted\",\"CombinedCreditLoans\"]\n",
        "sol=['SeriousDlqin2yrs']\n",
        "\n",
        "train_df = train_df[attributes]\n",
        "test_df = test_df[attributes2]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtILeMjLdqBV",
        "colab_type": "text"
      },
      "source": [
        "#### 2.4.1 逻辑回归"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ao4GUMXE-etT",
        "colab_type": "text"
      },
      "source": [
        "为了快速测试，写了一个类"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5dzr04l-d5A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "import os\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "class Tester():\n",
        "    def __init__(self, target):\n",
        "        self.target = target\n",
        "        self.datasets = {}\n",
        "        self.models = {}\n",
        "        self.cache = {} # 我们添加了一个简单的缓存来加快速度\n",
        "\n",
        "    def addDataset(self, name, df):\n",
        "        self.datasets[name] = df.copy()\n",
        "\n",
        "    def addModel(self, name, model):\n",
        "        self.models[name] = model\n",
        "        \n",
        "    def clearModels(self):\n",
        "        self.models = {}\n",
        "\n",
        "    def clearCache(self):\n",
        "        self.cache = {}\n",
        "    \n",
        "    def testModelWithDataset(self, m_name, df_name, sample_len, cv):\n",
        "        if (m_name, df_name, sample_len, cv) in self.cache:\n",
        "            return self.cache[(m_name, df_name, sample_len, cv)]\n",
        "\n",
        "        clf = self.models[m_name]\n",
        "        \n",
        "        if not sample_len: \n",
        "            sample = self.datasets[df_name]\n",
        "        else: sample = self.datasets[df_name].sample(sample_len)\n",
        "            \n",
        "        X = sample.drop([self.target], axis=1)\n",
        "        Y = sample[self.target]\n",
        "\n",
        "        s = cross_validate(clf, X, Y, scoring=['roc_auc'], cv=cv, n_jobs=-1)\n",
        "        self.cache[(m_name, df_name, sample_len, cv)] = s\n",
        "\n",
        "        return s\n",
        "\n",
        "    def runTests(self, sample_len=80000, cv=4):\n",
        "        # 在所有添加的数据集上测试添加的模型\n",
        "        scores = {}\n",
        "        for m_name in self.models:\n",
        "            for df_name in self.datasets:\n",
        "                # print('Testing %s' % str((m_name, df_name)), end='')\n",
        "                start = time.time()\n",
        "\n",
        "                score = self.testModelWithDataset(m_name, df_name, sample_len, cv)\n",
        "                scores[(m_name, df_name)] = score\n",
        "                \n",
        "                end = time.time()\n",
        "                \n",
        "                # print(' -- %0.2fs ' % (end - start))\n",
        "\n",
        "        print('--- Top 10 Results ---')\n",
        "        for score in sorted(scores.items(), key=lambda x: -1 * x[1]['test_roc_auc'].mean())[:10]:\n",
        "            auc = score[1]['test_roc_auc']\n",
        "            print(\"%s --> AUC: %0.4f (+/- %0.4f)\" % (str(score[0]), auc.mean(), auc.std()))\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yiJCg-yJBHG5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "ca01cd44-952a-4b5d-9028-d17e68c40bce"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# 我们将在所有模型中使用测试对象\n",
        "tester = Tester('SeriousDlqin2yrs')\n",
        "\n",
        "# 添加数据集\n",
        "tester.addDataset('Drop Missing', train_df.dropna())\n",
        "\n",
        "# 添加模型\n",
        "rfc = RandomForestClassifier(n_estimators=15, max_depth = 6, random_state=0)\n",
        "log = LogisticRegression()\n",
        "tester.addModel('Simple Random Forest', rfc)\n",
        "tester.addModel('Simple Logistic Regression', log)\n",
        "\n",
        "# 测试\n",
        "tester.runTests()"
      ],
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Top 10 Results ---\n",
            "('Simple Random Forest', 'Drop Missing') --> AUC: 0.8128 (+/- 0.0085)\n",
            "('Simple Logistic Regression', 'Drop Missing') --> AUC: 0.7884 (+/- 0.0107)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuKPZWkbBybM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = train_df.drop(['SeriousDlqin2yrs'], axis=1)\n",
        "Y_train = train_df['SeriousDlqin2yrs']\n",
        "\n",
        "X_test = test_df.drop([\"Unnamed: 0\"], axis=1)\n",
        "rfc.fit(X_train, Y_train)\n",
        "Y_pred = rfc.predict_proba(X_test)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_duLLrpHV0k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "submission = pd.DataFrame({\n",
        "        \"Id\": test_df[\"Unnamed: 0\"],\n",
        "        \"Probability\": pd.DataFrame(Y_pred)[1]\n",
        "    })\n",
        "\n",
        "submission.to_csv('submission.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iMxAlfatJlvq",
        "colab_type": "text"
      },
      "source": [
        "最终结果：得分0.81032，最高分是0.86955"
      ]
    }
  ]
}